Deriving End-Use Load Profiles Without End-Use Metering: Results of Recent Validation Studies

Obtaining end-use load profiles through metering can be costly and time-consuming. As a result, the cost of an end-use metered sample designed to support a study analyzing even a relatively small number of customer segments can be prohibitive. On the other hand, most utilities maintain large samples that are metered at the premise level. Usoro and Schick 1986 present statistical methods such as conditional demand analysis that have been used to disaggregate the premise-level load data into its major end-use components. Other approaches are discussed in Battles 1990; Eto and Akbari 1990; Parti et al. 1992; and Taylor and Pratt 1990. This paper presents the results of validation studies of using a heuristic pattern recognition algorithm to disaggregate premise-level load profiles. The results in this paper are based on validation studies performed after papers presenting the results of heuristic disaggregation published by Margossian and Ellison 1993; and Powers and Martinez 1992.